Recommender systems have been broadly used in various e-commerce applications such as targeted marketing, advertisement, and personalized search. Despite the widespread application of recommender systems, understanding user activities and therefore predicting user interest are still an open problem for many e-commerce vendors and service providers. For example, current recommender systems focus on building a model from previous behavior of users and use the model to predict products, or items, for recommendation. However, given dynamicity of interest of users in e-commerce, it would be difficult to accurately predict user interest by merely analyzing previous activities of the users.